Objective:To explore the diagnostic value of digital mammography combined with serum glutathione specific gamma-glutamyl transpeptidase 1(CHAC1)and retinoic acid-induced protein 14(RAI14)in identifying benign and malignant breast masses.Methods:A total of 189 patients with breast masses who were treated at Handan Maternal and Child Health Care Hospital from June 2019 to June 2024 were prospectively selected as the research subjects.According to the results of pathological biopsy,they were divided into benign mass group(128 cases)and malignant mass group(61 cases).All patients underwent digital mammography detection.The levels of serum CHAC1 and RAI14 were detected by enzyme-linked immunosorbent assay(ELISA).The general clinical data of the patients were collected and analyzed.Multivariate logistic regression analysis was used to analyze the factors of influencing benign and malignant nature of breast masses.The receiver operating characteristic(ROC)curve was drawn to analyze the diagnostic value of CHAC1 and RAI14 for the benign and malignant nature of breast masses.The Kappa test was used to assess the consistency of results between each diagnostic method and the pathological detection.Results:For 189 patients with breast masses,digital mammography identified 56 cases of malignant masses and 133 cases of benign masses,and 13 cases of them were misdiagnosis and 18 cases of them were missed diagnosis.It showed a moderate consistency with the results of pathological detection(Kappa=0.617,P<0.05).Compared with the benign mass group,the levels of serum CHAC1 and RAI14 in the malignant mass group were significantly higher,and the differences of them between the two groups were statistically significant(t=12.249,12.512,P<0.05).The age,menopausal time,CHAC1 and RAI14 of the patients were all risk factors that can affect the benign and malignant nature of breast masses(OR=1.368,1.305,1.897,1.995,P<0.05).The area under curve(AUC),sensitivity and specificity of CHAC1 were respectively 0.816(95%CI:0.753～0.868),70.49%and 89.06%in diagnosing the benign and malignant nature of breast masses.These indicators of RAI14 were respectively 0.838(95%CI:0.778～0.888),68.85%and 89.84%in diagnosing the benign and malignant nature.The combined detection of the three methods identified 74 cases of malignant masses and 115 cases of benign masses,with 15 cases of misdiagnosis and 2 cases of missed diagnosis,which showed an extremely high consistency with the results of pathological detection(Kappa=0.805,P<0.001).The sensitivity(96.72%),negative predictive value(98.26%)and accuracy(91.01%)of the combined detection of digital mammography,serum CHAC1 and RAI14 were significantly higher than those of each alone detection of them,and the differences of them were significant(x2=15.310,16.623,15.310,11.690,12.402,11.572,5.276,5.276,4.677,P<0.05).Conclusion:The levels of serum CHAC1 and RAI14 appear increase in malignant breast masses,and digital mammography combined with serum CHAC1 and RAI14 has a certain of identification value for benign and malignant nature of breast masses.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

OBJECTIVE: The objective of our study was to evaluate the vaccine effectiveness (VE) of the pentavalent rotavirus vaccine (RV5) among < 5-year-old children in three provinces of China during 2020-2024 via a propensity score-matched test-negative case-control study. METHODS: Electronic health records and immunization information systems were used to obtain data on acute gastroenteritis (AGE) cases tested for rotavirus (RV) infection. RV-positive cases were propensity score matched with RV-negative controls for age, visit month, and province. RESULTS: The study included 27,472 children with AGE aged 8 weeks to 4 years at the time of AGE diagnosis; 7.98% (2,192) were RV-positive. The VE (95% confidence interval, CI) of 1-2 and 3 doses of RV5 against any medically attended RV infection (inpatient or outpatient) was 57.6% (39.8%, 70.2%) and 67.2% (60.3%, 72.9%), respectively. Among children who received the 3rd dose before turning 5 months of age, 3-dose VE decreased from 70.4% (53.9%, 81.1%) (< 5 months since the 3rd dose) to 63.0% (49.1%, 73.0%) (≥ 1 year since the 3rd dose). The three-dose VE rate was 69.4% (41.3%, 84.0%) for RVGE hospitalization and 57.5% (38.9%, 70.5%) for outpatient-only medically attended RVGE. CONCLUSION Three-dose RV5 VE against rotavirus gastroenteritis (RVGE) in children aged < 5 years was higher than 1-2-dose VE. Three-dose VE decreased with time since the 3rd dose in children who received the 3rd dose before turning five months of age, but remained above 60% for at least one year. VE was higher for RVGE hospitalizations than for medically attended outpatient visits.
Humans ; Rotavirus Vaccines/immunology* ; China/epidemiology* ; Case-Control Studies ; Child, Preschool ; Infant ; Rotavirus Infections/epidemiology* ; Male ; Propensity Score ; Female ; Vaccine Efficacy ; Gastroenteritis/virology* ; Vaccines, Attenuated ; Rotavirus

To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S_p and the total row point count C_total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S_p, average height H_avg, mid-layer point cloud density C_m, and total row point count C_total as input variables, achieved the best performance, with a coefficient of determination (R_v²) of 0.713 and a root mean square error (RMSE_v) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.
Plant Leaves/growth & development* ; Saccharum/growth & development* ; Algorithms ; Unmanned Aerial Devices ; Remote Sensing Technology/methods* ; Crops, Agricultural/growth & development*

Objective:To investigate the impact of ginseng alcohol extract(GEE)on improving sleep quality in the aged Drosophila model by regulating the redox balance,and to elucidate its associated mechanism.Methods:Thirty-two male drosophila melanogaster(7-days-old)were randomly selected as young group,while 64 male Drosophila melanogaster flies(35-days-old)were randomly assigned to aged model group(n=32)and GEE group(n=32).The sleep parameters,including total sleep duration,daytime sleep duration,night sleep duration,0-4 h of sleep duration after lights off(ZT0-4 sleep duration),deep sleep duration,sleep episodetimes,sleep fragmentation,and the activity parameters such as the total number of locomotor activity daytime locomotor activity amount and nighttime locomotor activity amount were analyzed using the DAM2 Drosophila behavioral analysis system 7 d after administration.The grouping of the drosophila was as above,and there were 100 drosophila ineach group.The differentially expressed proteins in drosophila brain tissue were screened,identified,and functionally analyzed using two-dimensional fluorescence difference gel electrophoresis(2D-DIGE)and matrix-assisted laser desorption/ionization time of flight mass spectrometry(MALDI-TOF/TOF-MS)proteomic methods.The grouping of the drosophila was as above,and there were 100 drosophila in each group.The activities of superoxide dismutase(SOD),catalase(CAT),and glutathione peroxidase(GSH-Px)and the levels of lipid peroxidation product(MDA)in brain tissue of the drosophila were determined using assay kits.Results:Compared with young group,the total sleep duration daytime sleep duration and night sleep cluration of the drosophila in agaed group were decreased(P<0.05 or P<0.01);and the sleep rhythm amplitude was shortened.Compared with aged group,the total sleep duration and daytime and nighttime sleep durations of the drosphila in GEE group were lengthened(P<0.01).Compared with young group,the ZT0-4 sleep duration deep sleep duration and sleep fragment of the drosophila in aged group were decreased(P<0.05 or P<0.01),and the sleep rhythm amplitude was shortened.Compared with young group,the ZT0-4 sleep duration,deep sleep duration,and single sleep fragment of the drosphila in GEE group were significantly prolonged(P<0.01),and the sleep amplitude was increased.Compared with young group,there was no significant difference in diurnal spontaneous activity or total spontaneous activity of the drosophila in aged group(P>0.05),while the nocturnal spontaneous activity was significantly increased(P<0.05).Compared with aged group,the diurnal spontaneous activity,nocturnal spontaneous activity,and total spontaneous activity of the drosophila in GEE group were significantly decreased(P<0.05 or P<0.01).A total of 47 differentially expressed proteins were selected in the 2D-DIGE electrophoretic mapping.Compared with young group,the expressions of 47 differentially expressed protein sites in aged group were down-regulated mainly including glutathione S-transferase,peroxiredoxin 1 and dihydrolipoic dehydrogenase,which were related to redox balance.Compared with young group,the activities of SOD,CAT and GSH-Px in brain tissue of the drosophila in aged group were decreased(P<0.05 or P<0.01),and the level of MDA was increased(P<0.01);compared with aged group,the activities of SOD,CAT and GSH-Px in brain tissue of the drosphila in GEE group were increased(P<0.05 or P<0.01),and the MDA level was decreased(P<0.05).Conclusion:GEE has improvement effect on the sleep quality of aged drosophila,and its possible mechanism may be related to upregulating the activities of antioxidant enzymes,inhibiting the accumulation of lipid peroxidation products,and maintaining redox balance.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Aim To study the protective effect of the silibinin derivative Sil-1 on acute myocardial ischemia in SD rats and its mechanism of action.Methods Af-ter 18 hours of oxygen-glucose deprivation and treat-ment of H9c2 cells,the protective effect of Sil-1 on rat cardiomyocytes was examined.SD rats were treated 30 minutes before surgery,followed by 24 h ligation of the left anterior descending coronary artery.The cardiopro-tective effects of Sil-1 and its mechanisms for improving myocardial ischemic injury were investigated using pro-teomics technology.Results In vitro,compared with the control group,the activity of H9c2 cells in the mod-el group showed reduced cell viability,increased dead cells,elevated ROS and higher levels of LDH and in-flammatory cytokines TNF-α,IL-1β and IL-6 in the culture medium.Sil-1 could improve the above condi-tions to different degrees.In vivo,compared with the control group,rats in the model group showed signifi-cantly higher T waves on electrocardiogram,significant ischemic areas in the heart section,disorganized ar-rangement of cardiomyocytes,increased inflammatory factor infiltration and elevated CK,CK-MB,LDH and inflammatory factors TNF-α,IL-6 and IL-1β.Besides,NF-κB phosphorylation levels in myocardial tissue in-creased.Sil-1 improved the above conditions to varying degrees.The results of proteomics showed that 90 pro-teins were found between the control vs model group and the Sil-1 vs model group,and KEGG enrichment a-nalysis showed that MAPK,chemokines,VEGF and other signaling pathways were abundant.Western blot results showed that Sil-1 blocked the phosphorylation of ERK,JNK and p38 MAPK.Conclusions Sil-1 inhib-its the MAPK pathway by blocking the phosphorylation of JNK,ERK,and p38 MAPK,and achieves a protec-tive effect on rats with acute myocardial infarction.